Application of Spatial Sampling to Remote Sensing Monitoring of Forest Cover Area

Zhang, Ji Ping; Zhang, Lin Bo; Gong, Bin

doi:10.4028/www.scientific.net/amr.610-613.3732

Cited by 5 publications

(5 citation statements)

References 21 publications

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“…Then, we used the sampling data to verify the representativeness of the knowledge layer instead of the sample itself. This process effectively used previous knowledge and incorporated it into the calculation process of the model, thus avoiding bias and reducing our dependence on data sampling (Li and Wang, 2004;Zhang et al, 2013). Therefore, the reliability of the obtained result was high.…”

Section: Discussionmentioning

confidence: 98%

Sandwich mapping of rodent density in Jilin Province, China

Liu

et al. 2018

J. Geogr. Sci.

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Rodents are the main host animals that spread plague, and Spermophilus dauricus (S. dauricus) is the most common rodent in North China. In this study, a rodent density survey was carried out in China's Jilin Province from April to August 2005. Moran's I and semivariogram curves were used to investigate the spatial distribution characteristics of the sampling data. We found that the spatial auto-correlation index was low and failed to generate a meaningful semivariogram curve. In this case, commonly used interpolators, such as kriging, were not suitable for mapping density over the study area. However, the Sandwich model, which is based on spatial stratified heterogeneity, could be applied to our data. Our results showed that the type of soil and land use significantly influenced the distribution of rodent density, and the interactive effect of these variables was much stronger than that of each variable alone. The Sandwich-estimated rodent density map showed that rodent density increased from the southeast to the northwest in Jilin Province. Finally, a framework of a rodent density survey using the Sandwich model was introduced.

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Section: Discussionmentioning

confidence: 98%

Sandwich mapping of rodent density in Jilin Province, China

Liu

et al. 2018

J. Geogr. Sci.

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“…Therefore, a key step before conducting spatial sampling is to analyze the spatial autocorrelation of sample variables. Only survey subjects with significant spatial autocorrelation can the use of spatial sampling methods be scientific and feasible [68].…”

Section: Spatial Autocorrelation Analysis Of Survey Samplesmentioning

confidence: 99%

“…Considering the local spatial variability of the survey objects, we propose a new spatial SS method. Spatial stratification refers to dividing the overall unit into several types or layers based on its attribute characteristics and spatial continuity [23]. In traditional SS, the sample points have no spatial coordinate information, which leads to the stratified results being spatially discrete.…”

Section: Determination Of Stratified Variables In Stratified Samplingmentioning

confidence: 99%

“…The process of forest growth and development will be affected by competition, natural disturbance, environmental conditions, tree species regeneration, and other factors and their interactions, there is a significant spatial autocorrelation [22,23], which results in a non-random spatial distribution of forest biomass [24]. The traditional sampling methods based on classical statistics do not take into account the spatial autocorrelation of forest biomass [25], as well as the interaction of multiple factors in spatial pattern and distribution, which leads to an increase in investigation costs and low estimation accuracy [24].…”

Section: Introductionmentioning

confidence: 99%

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Sampling Estimation and Optimization of Typical Forest Biomass Based on Sequential Gaussian Conditional Simulation

Luo,

Xu,

et al. 2023

Forests

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The traditional classical sampling statistics method ignores the spatial location relationship of survey samples, which leads to many problems. This study aimed to propose a spatial sampling method for sampling estimation and optimization of forest biomass, achieving a more efficient and effective monitoring system. In this paper, we used Sequential Gaussian Conditional Simulation (SGCS) to obtain the biomass of four typical forest types in Shangri-La, Yunnan Province, China. In addition, we adopted a geostatistical sampling method for sample point layout and optimization to achieve the purpose of improving sampling efficiency and accuracy, and compared with the traditional sampling method. The main results showed that (1) the Gaussian model, exponential model, and spherical model were used to analyze the variogram of the four typical forests biomass, among which the exponential model had the best fitting effect (R2 = 0.571, RSS = 0.019). The range of the exponential model was 8700 m, and the nugget coefficient (C0/(C0 + C)) was 11.67%, which showed that the exponential model could be used to analyze the variogram of forest biomass. (2) The coefficient of variation (CV) based on 323 biomass field plots was 0.706, and the CV based on SGCS was 0.366. In addition, the Overall Estimate Consistency (OEC) of the simulation result was 0.871, which can be used for comparative analysis of traditional and spatial sampling. (3) Based on the result of SGCS, with 95% reliability, the sample size of traditional equidistant sampling (ES) was 191, and the sampling accuracy was 95.16%. But, the spatial sampling method based on the variation scale needed 92 samples, and the sampling accuracy was 93.12%. On the premise of satisfying sampling accuracy, spatial sampling efficiency was better than traditional ES. (4) The accuracy of stratified sampling (SS) of four typical forest areas based on 191 samples was 97.46%. However, the sampling accuracy of the biomass variance stratified space based on the SGCS was 93.89%, and the sample size was 52. Under the premise of satisfying the sampling accuracy, the sampling efficiency was obviously better than the traditional SS. Therefore, we can obtain the conclusion that the spatial sampling method is superior to the traditional sampling method, as it can reduce sampling costs and solve the problem of sample redundancy in traditional sampling, improving the sampling efficiency and accuracy, which can be used for sampling estimation of forest biomass.

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“…Na literatura, exemplos da utilização de amostragem probabilística não faltam. Os processos mais comuns, mesmo não sendo específicos para o recorte municipal, são: a aleatória simples (ZHANG et al, 2012;WEST, 2016), a sistemática (D'ORAZIO, 2003;HANSEN et al, 2013), a estratificada aleatória (SALEHI et al, 2010;TOMPPO et al, 2014) As informações geradas, em um menor tempo e custo (em relação ao censo demográfico), poderão auxiliar na caracterização do grupo de municípios que mais necessita de melhoria dos indicadores socioeconômicos, bem como de investimentos para a revegetação via plantios de espécies nativas.…”

Section: Introductionunclassified

Desflorestamento e desenvolvimento socioeconômico dos municípios brasileiros: considerações sobre a sustentabilidade do crescimento econômico e social

Santiago¹

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Desflorestamento e desenvolvimento socioeconômico dos municípios brasileiros: considerações sobre a sustentabilidade do crescimento econômico e social versão revisada de acordo com a resolução CoPGr 6018 de 2011 Orientador: Prof. Dr. HILTON THADEU ZARATE DO COUTO Tese apresentada para obtenção do título de Doutor em Ciências, Programa: Recursos Florestais. Opção em: Conservação de Ecossistemas Florestais Piracicaba 2019 Dados Internacionais de Catalogação na Publicação DIVISÃO DE BIBLIOTECA-DIBD/ESALQ/USP Santiago, Anderson Ribeiro Desflorestamento e desenvolvimento socioeconômico dos municípios brasileiros: considerações sobre a sustentabilidade do crescimento econômico e social / Anderson Ribeiro Santiago.-versão revisada de acordo com a resolução CoPGr 6018 de 2011.-Piracicaba, 2019. 101 p. Tese (Doutorado)-USP / Escola Superior de Agricultura "Luiz de Queiroz". 1. Florestas brasileiras 2. Representatividade amostral 3. Efetividade de políticas públicas I. Título SUMÁRIO RESUMO ……………………………………..……………………………….………..

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Application of Spatial Sampling to Remote Sensing Monitoring of Forest Cover Area

Cited by 5 publications

References 21 publications

Sandwich mapping of rodent density in Jilin Province, China

Sandwich mapping of rodent density in Jilin Province, China

Sampling Estimation and Optimization of Typical Forest Biomass Based on Sequential Gaussian Conditional Simulation

Desflorestamento e desenvolvimento socioeconômico dos municípios brasileiros: considerações sobre a sustentabilidade do crescimento econômico e social

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